CC BY-NC 4.0
The publisher's licence restricts use to research and other non-commercial purposes.
Files are served from OneDrive share links (RGB video, depth video, CAD models, annotations, camera parameters, hand pose) listed on the project page, with a Baidu Cloud mirror. No CLI; buyer clicks each component. Only the list in release.txt is public, the rest is a held-out test set. Data licence CC BY-NC 4.0, so no commercial use.
Specification, as published
- Hours (derived)
- 44 h
- Episodes (stated)
- 4,000 episodes
- Scale
- 2.4M RGB-D egocentric video frames over 4000 sequences
- Task
- Pick & place, Other
- Environment
- Indoor – home, Indoor – lab
- Modality
- RGB, Depth, Point cloud, Hand pose, Object pose
- Capture
- Egocentric
- In frame
- Person hands
- Captured in
- Asia
- Embodiment
- human
- Formats
- MP4, JSON, Pickle, Other
- Hosted on
- hoi4d.github.io
Figures are the publisher's own. Kinetic Blocks has not measured this dataset. Derived from the paper: 2.4M frames at 15 fps, summed across the two RGB-D sensors. Unique recording time is about 22 hours (4,000 sequences of 20 seconds).
About this dataset
HOI4D is a first-person RGB-D dataset built for category-level human-object interaction research. Nine participants interact with 800 object instances from 16 categories across 610 indoor rooms, giving 4,000 sequences and 2.4 million frames. Every frame carries action, motion and panoptic segmentation labels, 3D hand pose, category-level object pose and reconstructed object meshes. Part of the data is held back as a test set.
Publisher's own task names: category-level object pose tracking, 4D point cloud semantic segmentation, egocentric action segmentation, 3D hand pose, motion segmentation, panoptic segmentation.
What Kinetic Blocks did, and did not do
Indexed and linked from the publisher. Not hosted, verified or graded by Kinetic Blocks. The publisher's terms govern. The licence shown is the one stated on the publisher's page, linked above. Nothing on this page is a claim by Kinetic Blocks.
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